Using machine learning to reduce domestic violence

Using machine learning to reduce domestic violenceBy Kathleen Hickey Apr 05, 2016 Using machine-learning to forecast which accused perpetrators of domestic violence — particularly those whose crimes result in injuries — will be re-arrested on similar charges can cut such recidivism in half, according to a recent report. Machine learning used during the arraignment process prevented “well over” 1,000 domestic violence incidents annually in at least one large metropolitan area, according to authors Richard Berk, a professor of criminology and statistics in the School of Arts & Sciences and the Wharton School, and Susan B. Sorenson, director of the Evelyn Jacobs Ortner Center on Family Violence. For their study, “Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions,” Berk and Sorenson analyzed 28,646 domestic violence arraignments that…

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